code: def get_hyperparameter_search_space(
      feat_type: Optional[FEAT_TYPE_TYPE] = None,
      dataset_properties: Optional[DATASET_PROPERTIES_TYPE] = None,
  ) -> ConfigurationSpace:
      cs = ConfigurationSpace()
      q_min = UniformFloatHyperparameter(
          "q_min", 0.001, 0.3, default_value=0.25
      )
      q_max = UniformFloatHyperparameter(
          "q_max", 0.7, 0.999, default_value=0.75
      )
      cs.add_hyperparameters((q_min, q_max))
      return cs
  
file_path: auto-sklearn/autosklearn/pipeline/components/data_preprocessing/rescaling/robust_scaler.py
imports: from typing import Dict, Optional, Tuple, Union
  import numpy
  from ConfigSpace.configuration_space import ConfigurationSpace
  from ConfigSpace.hyperparameters import UniformFloatHyperparameter
  from scipy import sparse
  from sklearn.exceptions import NotFittedError
  from autosklearn.askl_typing import FEAT_TYPE_TYPE
  from autosklearn.pipeline.base import DATASET_PROPERTIES_TYPE, PIPELINE_DATA_DTYPE
  from autosklearn.pipeline.components.base import AutoSklearnPreprocessingAlgorithm
  from autosklearn.pipeline.components.data_preprocessing.rescaling.abstract_rescaling import Rescaling
  from autosklearn.pipeline.constants import DENSE, INPUT, SIGNED_DATA, SPARSE, UNSIGNED_DATA
label_desc: Use image node_id 3 for calling the RobustScalerComponent obj's underlying member method code with example usage: obj.get_hyperparameter_search_space(feat_type, dataset_properties) and returns: cs